Dubbed Inflame, BT says that its epidemiology-based prototype uses deep reinforcement learning to enable businesses to automatically detect and respond to cyberattacks before they compromise a network.
“Epidemiological testing has played a vital role in curbing the spread of infection during the pandemic, and Inflame uses the same principles to understand how current and future digital viruses spread through networks,” says Howard Watson, Chief Technology Officer, BT.
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BT says Inflame, which will be offered as part of its Eagle-i cyber defense platform. uses the principles of epidemiology to understand how computer viruses and cyberattacks spread across enterprise networks in order to prevent them from happening in the first place.
Inspired by the pandemic
To develop the technology, security researchers at the BT Labs in Ipswich, UK, first built models of enterprise networks that were then used to test various scenarios based on differing R rates of the cyber-infection. The R rate in epidemiology helps quantify the spread of infectious diseases in a population, explains BT.
Thanks to this testing, the researchers were able to understand how the threats penetrate and compromise a network, which helped them develop optimal automated responses that could contain and prevent the spread of viruses.
Furthermore, BT adds that these responses were underpinned by ‘attack lifecycle’ modelling, which assesses real-time security alerts against established patterns in order to understand the current stage of an ongoing cyberattack.
This insight, BT claims, helps the prototype predict the next stages of an attack, which it uses to identify the best response that will prevent the attack from progressing any further.
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With almost two decades of writing and reporting on Linux, Mayank Sharma would like everyone to think he’s TechRadar Pro’s expert on the topic. Of course, he’s just as interested in other computing topics, particularly cybersecurity, cloud, containers, and coding.